Learning Algorithms for Predictive Modeling in Biomedical Computing: Methods and Applications

生物医学计算中预测建模的学习算法：方法与应用

• 批准号: RGPIN-2020-07117
• 负责人: Mousavi, Parvin
• 金额: $ 3.5万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750625
• 关键词: Learning; Algorithms; Predictive; Modeling; Biomedical

Computer Aided Interventions (CAI) refer to systems that incorporate information from a multitude of biosensors, with computational algorithms to assist in decision making. CAI systems are a cornerstone of modern medicine and have played a significant role for the past few decades in areas such as radiology and robotic surgery. The vast amount of data generated from various biosensors and tools (e.g. imaging modalities) for CAI are complemented by unprecedented advances in computational approaches in particular machine learning for analysis of data. Understanding biomedical data has therefore been impacted fundamentally, mostly in a positive manner, with this trend expected to grow. However, in practice, the abundance of machine learning solutions stands in stark contrast to their uptake in decision making with practical impact. Of the significant challenges for realizing this are the noisy and highly heterogenous nature of the data from various modalities and populations, and the limited availability and inaccurate nature of annotations. The overarching goal of this proposal is to design the next generation of learning algorithms and critical decision-making approaches for actionable, optimized CAI that address the challenges to the uptake of the developed techniques in this domain. I propose innovative methods that: i) disentangle informative task-specific attributes of data from modality-specific attributes. Task-specific attributes can then be used to ensure knowledge learned from one domain of data (e.g. imaging modality such as raw US) is transferable to other domains (e.g. B-mode US). To fuse multiple modalities of data, flexible and efficient deep learning-based approaches that eliminate optimization during registration and provide uncertainty estimates are devised; ii) involve discriminative and generative approaches to learn from data with imprecise annotations through unsupervised discovery of associations between data points, and decision making using such associations in the context of limited available gold-standard annotations; iii) provide decision support for tissue classification using simultaneous theory-guided and data-driven learning. Theory-based, simulated data allow convergence to a solution and experimental data help minimize the residual error from the previous step, through unsupervised adversarial methods. The proposed research program will provide training for 3 PhD, 3 MSc and 5UG HQP. State of the art interdisciplinary training environment for HQP that follows the principals of Equity, Diversity and Inclusion will be provided. Trainees will acquire a wide spectrum of skills including image processing, machine learning, decision making and software development. They will learn to translate their knowledge of computing to high impact problems with practical implications. Their training will be of considerable value to a growing demand in both public and private sectors in machine learning and biomedicine.

计算机辅助干预 (CAI) 是指整合来自多个生物传感器的信息并通过计算算法辅助决策的系统。 CAI 系统是现代医学的基石，过去几十年在放射学和机器人手术等领域发挥了重要作用。用于 CAI 的各种生物传感器和工具（例如成像模式）生成的大量数据得到了计算方法（特别是用于数据分析的机器学习）空前进步的补充。因此，对生物医学数据的理解受到了根本性的影响，主要是积极的影响，而且这种趋势预计还会增长。然而，在实践中，丰富的机器学习解决方案与它们在决策中的实际影响形成了鲜明的对比。实现这一点的重大挑战包括来自不同模式和群体的数据的噪声和高度异质性，以及注释的可用性有限和不准确。 该提案的总体目标是为可操作、优化的 CAI 设计下一代学习算法和关键决策方法，以解决该领域已开发技术的采用所面临的挑战。我提出的创新方法是：i）将数据的信息性任务特定属性与模态特定属性分开。然后，可以使用特定于任务的属性来确保从一个数据域（例如，原始 US 等成像模式）学到的知识可以转移到其他域（例如，B 模式 US）。为了融合多种数据模式，设计了灵活高效的基于深度学习的方法，消除了配准过程中的优化并提供不确定性估计； ii) 涉及判别性和生成性方法，通过无监督地发现数据点之间的关联，从具有不精确注释的数据中学习，并在有限的可用黄金标准注释的情况下使用此类关联进行决策； iii) 使用理论指导和数据驱动的同步学习为组织分类提供决策支持。基于理论的模拟数据允许收敛到解决方案，而实验数据有助于通过无监督的对抗方法最大限度地减少上一步的残余误差。拟议的研究计划将为 3 名博士、3 名理学硕士和 5 名 UG HQP 提供培训。将为 HQP 提供遵循公平、多元化和包容性原则的最先进的跨学科培训环境。学员将获得广泛的技能，包括图像处理、机器学习、决策和软件开发。他们将学习将计算知识转化为具有实际意义的高影响力问题。他们的培训对于公共和私营部门对机器学习和生物医学不断增长的需求具有相当大的价值。